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08/17/06 - USPTO Class 706 |  177 views | #20060184482 | Prev - Next | About this Page  706 rss/xml feed  monitor keywords

Adaptive decision process

USPTO Application #: 20060184482
Title: Adaptive decision process
Abstract: An adaptive decision process is disclosed for more effectively and efficiently determining and conducting information gathering and evaluation associated with decisions. The adaptive decision process integrates decision analysis, value of information analysis, design of experiment models, and the inferencing of gathered information, including experimental results. The process enables an automatic, adaptive, closed-loop process for attaining additional information and assimilating the attained information into the decision model. (end of abstract)



Agent: Manyworlds, Inc. - Houston, TX, US
Inventors: Steven Dennis Flinn, Naomi Felina Moneypenny
USPTO Applicaton #: 20060184482 - Class: 706045000 (USPTO)

Related Patent Categories: Data Processing: Artificial Intelligence, Knowledge Processing System

Adaptive decision process description/claims


The Patent Description & Claims data below is from USPTO Patent Application 20060184482, Adaptive decision process.

Brief Patent Description - Full Patent Description - Patent Application Claims
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CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims priority under 35 U.S.C. .sctn.119(e) to U.S. Provisional Patent Application Ser. No. 60/652,578, entitled "Adaptive Decision Process," filed on Feb. 14, 2005.

FIELD OF INVENTION

[0002] This invention relates to decision processes and, more particularly, to processes and associated methods and computer-based programs in which probabilistic inferencing and experimental design are applied to support decision processes.

BACKGROUND OF THE INVENTION

[0003] Many decisions are influenced by some element of uncertainty. It is often valuable to take actions to gather information that may, at least in part, resolve uncertainties associated with a decision. Some calculation methods associated with determining the value of perfect or imperfect information are known from prior art. For example, the application of decision tree techniques may enable the derivation of expected values of information associated with an information gathering action. These methods typically require significant manual modeling efforts.

[0004] Experimental design or "design of experiment" methods are also known from prior art. These are methods of organizing experiments, or more broadly, any type of information gathering actions, in a manner so as to maximize the expected value of the resulting information, typically in accordance with constraints, such as an action budgetary constraint. For example, factorial matrix methods are a well established approach to scientific experimental design. These types of design of experiment methods typically require a statistician or other human expert to manually establish the experimental design parameters, and the proper sequencing of the experiments.

[0005] Making inferences from information attained as a result of experiments or, more broadly, information gathering actions, is well known from prior art. For example, in the prior art, a wide variety or statistical techniques are known and may be applied. These statistical techniques generally require some degree of interpretation by a statistician or other expert to be applied to decisions. And, in the prior art, a limited ability to automatically conduct experimental or information gather actions is known, but the application is invariably constrained by the requirement of human intervention to interpret interim results and adjust the experimentation accordingly.

[0006] Thus, in the prior art, each of the steps of determining expected value of information, of experimental design, of conducting experimentation, and of performing statistical or probabilistic inferencing from new information generated by experimentation, requires significant human intervention. Furthermore, in prior art processes, there does not exist an automatic feedback loop from the inferencing from new information step to the value of information and experimental design steps. This introduces significant bottlenecks in addressing and resolving uncertainties associated with decisions efficiently and effectively. This deficiency of the prior art processes and systems represents a particularly significant economic penalty in situations in which large amounts of relevant information is already available, or can be gathered rapidly. For example, high throughput experimentation methods can enable rapid acquisition of new information. However, manual bottlenecks may effectively limit the actually attainable throughput of such experimental infrastructure, and, more generally, limit the most effective use of available historical information.

[0007] The economic penalties associated with prior art decision processes are particularly acute in business processes such as product and/or service research and development, for which the manual interventions required in decision processes diminish both the efficiency and the effectiveness (measured in both quality and timeliness) of the decision making.

[0008] Hence, there is a need for an improved process, method, and system to resolve uncertainties associated with decisions.

SUMMARY OF INVENTION

[0009] In accordance with the embodiments described herein, a method and system for an adaptive decision process is disclosed. The adaptive decision process, as the process is known herein, addresses the shortcomings of the prior art by enabling an automatic closed loop approach to information gathering decisions and the evaluation of the results of the information gathering.

[0010] Other features and embodiments will become apparent from the following description, from the drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011] FIG. 1 is a block diagram illustrating an adaptive decision process, according to some embodiments;

[0012] FIG. 2 is a block diagram showing further details of an adaptive decision process according to some embodiments;

[0013] FIG. 3 is a flow diagram of the adaptive decision process of FIG. 2, according to some embodiments;

[0014] FIG. 4 is a block diagram of the adaptive decision process of FIG. 2, with an alternative representation of a decision model, according to some embodiments;

[0015] FIG. 5 is a diagram of an uncertainty resolution value framework, according to some embodiments;

[0016] FIG. 6A is a diagram of an uncertainty resolution cost framework, according to some embodiments;

[0017] FIG. 6B is a diagram of specific information sources within the uncertainty resolution cost framework of FIG. 6A, according to some embodiments;

[0018] FIG. 7 is a diagram of a net action value framework, according to some embodiments;

[0019] FIG. 8 is a diagram of an uncertainty mapping applied by the adaptive decision process of FIG. 2, according to some embodiments;

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